Power cell tracking and optimization system

ABSTRACT

A computing system can receive and compile power cell data, and in certain examples, the power cell data can be distributed to a distributed ledger. The computing system can further determine approximate battery end of life (ABEL) for each power cell based on a compiled historical record of power cell data. Based on the determined ABEL, the computing system can generate ABEL reports for users, determine optimal settings for a power cell or battery-powered device, and/or transmit notifications to users, to facilitate power cell usage optimization, and/or optimal repurposing or recycling timing.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. ProvisionalApplication No. 62/718,878, filed on Aug. 14, 2018, which is herebyincorporated by reference in its entirety.

BACKGROUND

Examples described herein relate generally to power cell tracking andoptimization systems for use in primary life power cells, second lifepower cells, and further applications of power cells (e.g., batteriesand/or energy storage systems). Battery systems and battery-poweredgoods, such as electric vehicles, tools, sensors Internet of Things(IoT) devices, etc., are projected to experience significant growth inproduction and sales in the coming decades. For example, commonforecasts for the lithium ion battery market predict growth of three tofour times current production in the next decade. However,underutilization of battery potential is common.

For example, widespread inefficiencies exist in the full usage ofbattery life, resulting in common practice of disposing or recyclingbatteries when they still possess nearly 80% of their capacity. Thisapproximate battery capacity coincides with what is commonly known asthe end of the primary life of the battery, and recycling or disposal ofthese batteries at the end of their primary life can results insignificant waste and environmental hazard.

A secondary market for so-called second life batteries or battery packsalleviates the harmful effects of premature disposal or recycling ofprimary life batteries. However, inefficiencies in each of primary lifebattery monitoring, primary battery life usage, end-of-life prediction,and the selection of second-life batteries for battery packs—as well asthe unreliability of battery information for used batteries and batteryproducts—have prevented the exploitation of the full potential ofbatteries.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure herein is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements, and in which:

FIG. 1 is a block diagram illustrating computing system implementingpower cell tracking and optimization, in accordance with examplesdescribed herein;

FIG. 2 is a flow chart describing an example method of power celltracking and optimization, according to various examples;

FIG. 3 is a flow chart describing an example method of monitoring powercell data in accordance with a set of optimization metrics and providingoptimization recommendations and/or control commands to increase ormaximize approximate battery end-of-life (ABEL) for batteries orbattery-powered devices, according to example described herein;

FIG. 4 is a flow chart describing an example method of monitoring powercell data in accordance with a set of end-of-life determination metricsand providing second-life repurposing or battery replacementrecommendations, according to examples described herein;

FIGS. 5A and 5B illustrate example user interfaces providing currentbattery state information (e.g., an ABEL report) and battery-checkrating and certificate, according to examples described herein; and

FIG. 6 is a block diagram that illustrates a computer system upon whichexamples described herein may be implemented.

DETAILED DESCRIPTION

A power cell tracking and optimization system is described herein andprovides a technical solution to the above-discussed problem in thefield of power cell technology. The power cell tracking and optimizationsystem can monitor and/or track power cell data from batteries,battery-powered devices, energy storage systems, etc. (e.g., of electricor hybrid automobiles). In various implementations, the system caninclude a database comprising data logs or profiles comprising batteryinformation from each power cell or power cell type that the systemmonitors. According to examples provided herein, the power celloptimization system can receive battery data from the battery-powereddevices periodically or dynamically and securely and permanently storethe data in the data logs. In certain examples, the power celloptimization system can operate as an independent entity to variousbattery-powered product manufacturers to remain unbiased and trusted toproduct owners, potential battery product purchasers, manufacturers,and/or future battery purchasers.

In various implementations, the system can organize the battery datacollected from each power cell, battery management system, or any othersystem that tracks, monitors, or manages power cells. For example, thesystem can collect or otherwise receive initial battery data from thenameplate or manufacturer of the power cell. For each power cell, theseinitial battery data can comprise information corresponding to thechemistry parameters of the power cell, the entity name of the producer,the type of battery cell chemistry, time of production (e.g., day, week,month, and/or year), charging and discharging temperatures, stated orestimated calendar life, estimated cycles, c-rate (e.g., a rate at whichthe battery is discharged relative to its maximum capacity), cellvoltage (e.g., minimum and maximum voltages), energy density, depth ofdischarge, and any other available data from the power cell and powercell producer.

The system can further collect data provided from a producer and/orintegrator that developed, tested, or otherwise installed the power cellinto a power usage source (e.g., a vehicle or energy storage system). Inaddition to the above-mentioned data, these additional data can includenominal capacity of the power cell solution, usable capacity of thepower cell solution, data about the setup of the battery managementsystem (BMS) of the power cell (e.g., limitations on charging speed,discharging, etc.), general parameters about power cells and theirperformance for each particular battery chemistry (e.g., industrystandards), and the like.

In certain aspects, the system can further collect time series data(e.g., atomic and/or aggregated data from the BMS of the power cell).For example, the system access data from the BMS of the power cell.These data can include current limits (e.g., minimum and maximumcurrents calculated by the BMS for charge and discharge directions), thecurrent read from a direct current bus (e.g., current minimums,maximums, and averages), stack voltage of the power cell(s) (e.g.,voltage minimums, maximums, and averages), cell voltage statistics(e.g., minimum, maximums, and average cell voltages of all power cells),cell voltage locations (e.g., cell number) for minimum and maximummeasurements, temperature statistics (e.g., minimum, maximum, andaverage temperatures measured), temperature measurement locations (e.g.,cell number for the minimum and maximum temperature measurements),contactor states, and the like. The system can further collect datacorresponding to, for example, state of charge, state of discharge,state of health and/or other data that may be calculated by BMS. Incertain implementations, the system can further collect data indicatingBMS safety (e.g., a Boolean indicator indicating whether the BMS is safeor not). For example, the contactors of the power cell can automaticallyopen if the BMS is determined to be unsafe. In addition, the system canfurther collect fault data of each power cell or the BMS (e.g., a bitfield of BMS faults for identification).

In certain examples, the power cell optimization system can collectinformation corresponding to the deployment of the power cell(s), suchas the parametric information relating to the use of the power cell(e.g., distance traveled in a respective electric vehicle, based onodometer reading), as well as the type of application which the powercell was (or is to be) used for (e.g., electric vehicle, batteryelectric vehicle, hybrid electric vehicle, electric bus, electricwatercraft, electric airplane, electric scooter, train, forklift, energystorage systems, home storage, battery-powered sensor, industrialstorage, photovoltaic connected, grid connected storage, power station,mobile phone, mobile device, IoT device, power tool, drone, and thelike). In some aspects, the system can further collect data indicatingthe battery mode (e.g., island mode), frequency regulation, and/or timeshift of a given power cell.

The data can be accessed or otherwise received using a controller areanetwork (CAN) communication protocol, ModBus communication protocol(like ModBus TCP or any other) or any other communication protocol. Invariations, the data can be accessed or received over one or morenetworks using any type of communications protocol (e.g., wireless orwired networks). For example, the power cell optimization systemdescribed herein can operate as a modular device in communication withthe BMS of a power cell stack (e.g., mounted on-board the vehicle), or aremote power cell optimization system that receives data over networkcommunications. In certain aspects, the power cell tracking andoptimization system can receive the power cell data from vehiclemanufacturers and/or the power cell manufacturers (e.g., producers ofelectric vehicles and batteries/energy storage systems). It iscontemplated that the battery-powered product manufacturers and powercell manufacturers may collect and store power cell data for theirmanufactured products and battery systems. In this case, the power celltracking and optimization system can connect to the data storage systems(e.g., cloud servers or central databases) of the manufacturers ortrusted third-party storage service providers to acquire the power celldata.

In other examples, the power cell optimization system can comprise adistributed computing environment (e.g., blockchain or other distributedledger technology) including remote and local computing systems thatwork together to independently collect and store battery data in acollectively guaranteed, reliable, secure, and robust manner (e.g., todetermine second life information for the battery, which can be utilizedfor a variety of battery optimizations).

The power cell tracking and optimization system can include continuousand permanent recording, storage, and analyses of the above-mentioneddata. According to examples described herein, the data can betransferred to central data storage, a combined central storage anddistributed ledger, or a distributed ledger or blockchain (e.g., privateblockchain, public blockchain, centralized blockchain, decentralizedblockchain, hybrid public/private blockchain, and the like) to ensuresafe and reliable storage. Additionally or alternatively, the power celloptimization system can analyze the data to determine a set of secondlife battery parameters indicating the usable capacity of the battery,number of cycles used, number of remaining cycles, and an approximatebattery end life (ABEL) (e.g., an amount of time or remaining cyclesleft in the battery). For example, the determination of the approximatebattery end life can be utilized as a starting point for second lifebattery usage.

In further implementations, the power cell optimization system candetermine effective combinations of various batteries with differentchemistries and generate a second life battery report indicating idealcombinations, which can be used by second life battery users to assembleand/or develop second life energy storage systems. For example, thepower cell optimization system can categorize power cells based on ABELcalculations such that power cells with the same or similar ABELs can beassembled together in energy storage systems or battery stacks such thateach power cell is utilized to its fullest potential.

In various examples, the data analysis and reporting can be implementedthrough machine learning, deep learning, statistical techniques, otherforms of data analytics, neural network techniques, and/or artificialintelligence techniques to provide an accurate status of each primarylife or second life power cell and to continually increase the accuracyand robustness of such determinations. In certain aspects, the powercell optimization system can collect data from new batteries (e.g., newbatteries installed in electric vehicles, battery powered devices andenergy storage systems), such that a full historical record of eachpower cell can be compiled. The power cell optimization system can beimplemented through internet of things (IoT) technology to record,transfer, and store battery-related data from battery managementsystems, sensors, or other battery-powered devices and products in aguaranteed secure storage mechanism. For example, the power celloptimization system can utilize distributed ledger technology to ensurethat the data are consistent and unalterable. As such, the data logs foreach power cell can include unique identifiers indicating the source ofthe data (e.g., which data from which battery were collected) andtime-stamps indicating when the data were collected. In addition, thedata logs can include the ABEL of the power cell, which can bedetermined periodically (e.g., after each iteration of data collection)or dynamically (e.g., for continuous data collection implementations).

According to some examples, the power cell optimization system canprovide the ABEL of each power cell to requesting users (e.g., an owneror operator of a vehicle), or second life power cell assemblers orentities using the ABEL reports to assemble second life batterysolutions, scenarios, energy storage systems, etc. from used batteries.In some examples, the ABEL reports can be accessed through a webinterface or via a designated application executing on computing devicesof the users.

In further implementations, the power cell tracking and optimizationsystem described herein can operate as a direct communication servicefor monitoring battery conditions, performance, capacity, etc., andprovide battery-powered device servicers or technicians with usagerecommendations (e.g., to replace a battery, to adjust chargingtechnique, or suggest recycling) along with contextual informationregarding the history of the battery (e.g., from a full report of thebattery, which is stored in the distributed ledger). In variousexamples, the battery-powered devices can communicate directly with thepower cell tracking and management system, providing updated informationregarding the battery metrics described herein (e.g., current charge,number of total charging cycles, ambient temperature, internaltemperature, and the like).

The power cell tracking and optimization system can store this updatedinformation on the distributed ledger, compare the updated informationwith previous data corresponding to the battery-powered device,determine one or more recommendations for an owner, operator, ortechnician of the battery-powered device, and provide therecommendation(s) accordingly (e.g., through an application programnotification on a computing device of the owner, operator, ortechnician). As provided herein, these recommendations can be providedto decrease a degradation rate of the battery, and optimize the poweroutput, operating conditions, performance, and ultimately the ABEL ofthe batteries that run the battery-powered device.

Among other benefits, examples described herein achieve a technicaleffect of optimizing power cell usage through power cell data analyticsand/or machine learning techniques to determine reliable and accuratepower cell usage recommendations and end-of-life for power cells, whileincreasing the accuracy and reliability of such determinations. Invarious implementations, examples described herein can leverage theadvantages of distributed ledger technology to ensure robustness andimmutability of data logs.

As provided herein, a “calendar life” of a power cell comprises theamount of time a power cell will last when used within normalboundaries. Such calendar life may also be referred to as nameplatecalendar life, which is chemistry specific. For example, LithiumTitanate Oxide batteries have a calendar life of fifteen to twentyyears, whereas Lithium Graphite NMC batteries have a calendar life often to twelve years.

Numerous examples described herein refer to a “power cell,” whichcomprises any combination of batteries, battery packs, battery cells,electrochemical devices, ultracapacitors, fuel cells, solar cells (e.g.,photovoltaic cells, solar water heaters, thermogalvanic cells, solar airheaters, solar thermal collectors, etc.), battery racks or batterystrings, battery modules, battery containers, and other energy storageand/or deployment systems and other energy producing systems (e.g.,piezoelectric sensors or devices, energy harvesting devices, crystals,etc.).

As further provided herein, “end-of-life,” “real end-of-life,” or “realend life” refers to the state of charge and/or state of health of apower cell being at zero percent. For primary life batteries, commonpractice is deemed that between 60%-80% of capacity equals the end ofthe primary life or simply end-of-life of the power cell. A “second lifebattery” comprise a power cell that has reached its primary end of life(e.g., 80% capacity for vehicle batteries and batteries/energy storagesystems), but still has usable capacity for secondary use. The“approximated battery end of life” (ABEL) of a second life battery,refers to an estimated or calculated time between the primary end oflife and the real end-of-life, and is the focus of the presentdisclosure.

As used herein, “distributed ledger” or “distributed ledger technology”refers to replicated, shared, and/or synchronized data spread acrossmultiple sites. In certain aspects, the distributed ledger can comprisea central storage combined with independent remote storage resources. Invariations, the distributed ledger can comprise a peer-to-peer networkof storage devices, where each device replicates and stores an identicalcopy of the ledger (e.g., power cell logs) and updates independently.Examples of distributed ledgers can include a blockchain, various typesof acyclic graphs (e.g., blockDAG or TDAG), and the like.

As used herein, a computing device refers to devices corresponding toservers, desktop computers, cellular devices or smartphones, personaldigital assistants (PDAs), laptop computers, virtual reality (VR) oraugmented reality (AR) headsets, tablet devices, etc., that can providenetwork connectivity and processing resources for communicating with thesystem over a network. A computing device can also correspond to customhardware, in-vehicle devices, or on-board computers, such as thoseprovided in connection with battery management systems of vehicles,energy storage systems, and the like. The computing device can alsooperate a designated application configured to communicate with anetwork service.

One or more examples described herein provide that methods, techniques,and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or more memoryresources of the computing device. A programmatically performed step mayor may not be automatic.

One or more examples described herein can be implemented usingprogrammatic modules, engines, or components. A programmatic module,engine, or component can include a program, a sub-routine, a portion ofa program, or a software component or a hardware component capable ofperforming one or more stated tasks or functions. As used herein, amodule or component can exist on a hardware component independently ofother modules or components. Alternatively, a module or component can bea shared element or process of other modules, programs or machines.

Some examples described herein can generally require the use ofcomputing devices, including processing and memory resources. Forexample, one or more examples described herein may be implemented, inwhole or in part, on computing devices such as servers, desktopcomputers, cellular phones or smartphones, personal digital assistants(e.g., PDAs), laptop computers, VR or AR devices, network equipment(e.g., routers), and/or tablet computers. Memory, processing, andnetwork resources may all be used in connection with the establishment,use, or performance of any example described herein (including with theperformance of any method or with the implementation of any system).

Furthermore, one or more examples described herein may be implementedthrough the use of instructions that are executable by one or moreprocessors. These instructions may be carried on a non-transitorycomputer-readable medium. Machines shown or described with figures belowprovide examples of processing resources and computer-readable mediumson which instructions for implementing examples disclosed herein can becarried and/or executed. In particular, the numerous machines shown withexamples of the invention include processors and various forms of memoryfor holding data and instructions. Examples of computer-readable mediumsinclude permanent memory storage devices, such as hard drives onpersonal computers or servers. Other examples of computer storagemediums include portable storage units, flash memory (such as carried onsmartphones, multifunctional devices, or tablets), and/or magneticmemory. Computers, terminals, network-enabled devices (e.g., mobiledevices, such as cell phones) are all examples of machines and devicesthat utilize processors, memory, and instructions stored oncomputer-readable mediums. Additionally, examples may be implemented inthe form of computer-programs, or a computer usable carrier mediumcapable of carrying such a program.

System Description

FIG. 1 is a block diagram illustrating an example computing system 100implementing power cell tracking and optimization, according to variousexamples. The computing system 100 can include a power cell interface105 that receives power cell data from the battery management systems ofany one or more of various sources, such as energy storage systems 192,vehicles 194, battery manufacturers 196, or any other solution whichuses a battery/power cell as a primary or secondary source of electricenergy.”. Each power cell, battery stack, or energy storage system 192can be managed by a battery management system which monitors batteries(e.g., voltage, temperature, current, etc.) and provide and calculateuseful data about the status of the batteries (e.g., state of charge,state of health, etc.).

The battery management system can also control and manage charging anddischarging of the power cells, influencing the battery performance andcapacity during the life of the power cell, protecting the battery fromdegradation, and maintaining the battery within a certain set of safetyparameters. While many examples provided with FIG. 1 and elsewhere inthis application are described specifically in context of electricalvehicles 194, in variations, the examples described are applicable toother battery-powered devices 193 which utilize a battery/power cell asa primary or secondary source of electric energy (e.g., electricvehicle, battery electric vehicle, hybrid electric vehicle, electricbus, electric watercraft, electric airplane, satellites, scooter, train,forklift, energy storage systems, home storage, industrial storage,photovoltaic connected storage, grid connected storage, power station,mobile phone, mobile device, IoT device(s), power tool, drone, leafblower, and the like).

As secondary batteries are used in a variety of manners, there arevarying requirements for battery management systems, and batterymanagement system topology depends what hardware and software componentsare used for the power cells, and can be based on the final energystorage solution and its targeted use. In various implementations, thepower cell interface 105 can communicate with communication resourcesproviding access to battery management systems of power cells (e.g., asimplemented on vehicles 194, energy storage systems 192, battery-powereddevices 193, power stations, or other solutions that use battery/powercell as a primary or secondary source of electric energy) to receive thepower cell data. Examples as described may be applicable to monitoringpower cells during first life, second life or any subsequent additionallife.

As described herein, the power cell data can comprise nameplateinformation of the power cell, such as the chemistry parameters of thepower cell, the entity name of the producer, the type of battery cellchemistry, time of production, temperature tolerances, stated orestimated calendar life, estimated cycles, c-rate, cell voltage ratings,energy density, depth of discharge, and any other available data fromthe power cell manufacturer 196. As further described herein, the powercell data can further include nominal capacity of the power cellsolution (e.g., total capacity), usable capacity of the power cellsolution (e.g., permitted capacity), data corresponding to the setup ofthe battery management system of the power cell solution (e.g.,limitations on charging speed, discharging, etc.), general parametersabout power cells and their performance for each particular batterychemistry (e.g., industry standards), and the like.

In certain aspects, the power cell data can further include time seriesdata (e.g., atomic and/or aggregated data from the BMS of the powercell). such as current limits, the current read from a direct currentbus, stack voltage of the power cell(s), cell voltage statistics, cellvoltage locations, temperature statistics, temperature measurementlocations, contactor states, and the like. The power cell data canfurther indicate a battery management system safety factor, and faultdata of each power cell. In certain examples, the power cell data canfurther comprise information corresponding to the deployment of thepower cell(s), such as the usage of the power cell, distance traveled(e.g., mileage from an odometer), and the end-user of the power cell(e.g., electric vehicle, battery electric vehicle, hybrid electricvehicle, electric bus, electric watercraft, electric airplane, scooter,train, forklift, mobile phone, mobile device, IoT device, power tool,drone, energy storage systems, home storage, industrial storage,photovoltaic connected, grid connected storage, power station, and thelike). In some aspects, the power cell data can also indicate thebattery mode (e.g., island mode), frequency regulation, and/or timeshift.

In certain implementations, the power cell interface 105 can receiveadditional data from the battery-powered devices 193 and/or vehicles194. For example, the power cell interface 105 can receive or otherwiseassess sensor data from vehicle sensors or sensors from a batterypowered device, such as diagnostic or telemetry sensors, an inertialmeasurement unit, positioning system, battery and/or energy storagesystem, and the like. Such data may also originate from the vehicle'son-board computers or memory, and can identify any faults, servicerequirements, current operability, and the like. In such examples, thecomputing system 100 can perform additional calculations with differentresults than ABEL, such as where power cell data is not needed as aprimary or necessary source of data. For example, a vehicle manufacturermay provide data from other sensors of their vehicles, enabling thecomputing system 100 to calculate failures of certain spare parts orvehicle systems, and/or calculate predictive maintenance for additionalcomponents of the vehicle 194.

The power cell data can be accessed by the power cell interface 105using a controller area network (CAN) communication protocol, ModBuscommunication protocol (like ModBus TCP), or any other communicationprotocol (e.g., leveraging JavaScript Object Notation (JSON) or anyother communication means). In variations, the power cell data can beaccessed or received by the power cell interface 105 over one or morenetworks 180 using any type of communications protocol (e.g., wirelessor wired networks).

In various examples, the computing system 100 can include a power celldata compiler 120 which can organize and store the power cell data intodata logs 147 of a database 145. In some aspects, the database 145 cancomprise a central storage facility for the data logs 147 (e.g., bigdata storage). In certain examples, the power cell data compiler 120 canassociate each power cell with a unique identifier (e.g., a vehicleidentification number of a vehicle in which the power cell isinstalled). In variations, the data compiler 120 can associate eachenergy storage system 192, battery-powered device 193, vehicle 194,battery string, battery stack, or other end usage object in which morethan one power cell is packaged, with a single unique identifier formanaging the power cell data received from the power cells in the datalogs 147.

In various aspects, the computing system 100 can further include adistributed ledger interface 115 that transmits the compiled power celldata and or the data logs 147 for each power cell or power cell packageto a distributed ledger 185 (e.g., a blockchain) to ensure that the dataand/or data logs 147 are secure and completely reliable. As providedherein, the distributed ledger 185 can comprise a peer-to-peer networkof nodes or computing systems executing one or more consensus computermodels or algorithms to ensure that the data logs 147 are replicatedacross the nodes.

According to examples described herein, the computing system 100 canfurther include an end-of-life (EoL) machine learning engine 140, whichcan execute one or more machine learning models to determine ABELs forpower cells, and to continuously improve the accuracy of thesecalculations. It is contemplated that the EoL machine learning engine140 can determine an ABEL for each power cell or power cell package(e.g., of a vehicle 194) based on the historical power cell datarecorded in the data logs 147 by the data compiler 120. For example,each iteration of data collection by the power cell data compiler 120can trigger the EoL machine learning engine 140 to determine a new orupdated ABEL for a given power cell of an ESS 192, battery-powereddevice 193, electric vehicle 194, and the like. As another example, theEoL machine learning model 140 can be triggered to determine an ABEL ofa particular power cell or energy storage system 192 upon request from auser 177 or other entity that uses, views or processes output of thecomputing system 100.

The EoL machine learning engine 140 can determine ABELs for a particularpower cell given the current data in the data log 147 of that powercell. As described, these data may be organized using a uniqueidentifier for the power cell and timestamps indicating when the datawere collected. Accordingly, the EoL machine learning engine 140 candetermine an ABEL for a battery given the entire historical record ofthe battery. The EoL machine learning engine 140 can also access orreceive, as learning input, new power cell data periodically ordynamically received from the battery management systems, communicationor processing interface, and/or IoT chips of the usage sources (e.g.,energy storage systems 192, battery-powered devices 193, electricvehicles 194, power stations, etc.). The EoL machine learning engine 140can utilize any newly received data to confirm or adjust previous ABELcalculations for a given power cell in order to continuously improvesuch calculations.

The EoL machine learning engine 140 can comprise computational resourcesexecuting a set of machine learning models or algorithms having a goalof accurate and reliable ABEL determinations. Such ABEL determinationcan be utilized by primary and or secondary power cell markets topromote the full usage of power cell capacity and significantly reducingand/or eventually eliminating waste. In providing accurate,unmanipulated and immutable ABEL reports to users 177—such asprospective used vehicle owners, current vehicle 194 owners, second lifebattery assemblers, prospective used battery-powered device buyers,current owners of battery-powered devices 193, or other entities thatmay otherwise use, view or process the output of the computing system100—the computing system 100 and distributed ledger 185 offers atechnical solution to various technical problems existing in the fieldof secondary power cell use.

The ABEL reports provided by the EoL machine learning engine 140 cancomprise ABEL calculations or determinations for individual power cells,which can be identified by their unique identifiers. The ABEL reportscan further include additional data, such as a battery state of healthcertificate (see FIG. 5B), which, in certain implementations, cancomprise a ratio of the current rating of a battery with respect tonameplate rating as indicated by the battery's manufacturer 196, and/ora battery health grade indicating the current rating along with aguaranteed certificate of reliability. For example, when the state ofhealth for a battery or battery-powered device 193 is 100% or “A+,” thebattery is performing in accordance with its nameplate rating. Asanother example, when the state of health for a battery orbattery-powered device 193 is within 95% of its nameplate rating, thebattery health grade can indicate an “A” rating.

For vehicle and battery-powered device implementations, the ABEL reportgenerated by the EoL machine learning engine 140 can include ABELdeterminations for every power cell in the vehicle 194 orbattery-powered device 193, or a collective ABEL for the entire batterypack of the vehicle 194 or battery-powered device 193. The ABEL reportof a vehicle 194 or battery-powered device 193 can further includeinformation indicating a current age of the battery system, a number ofcharge/discharge cycles of the vehicle's battery system, and estimatednumber of charge/discharge cycles left, an estimated calendar liferemaining for the battery system, the current mileage of the vehicle 194or hourly usage of a battery-powered device 193, and the like. Alongthese lines, the ABEL report for any power cell can also include acurrent age of the power cell, a number of charge/discharge cycles ofthe power cell, an estimated number of charge/discharge cycles left, anestimated calendar life remaining for the power cell, a performancegrade for the power cell, and the like. It is contemplated that suchreports can be utilized to more accurately determine a current valuationof a vehicle 194 or battery-powered device 193 for both current ownersand prospective buyer and largely eliminate the current need forspeculative valuation.

In certain implementations, the users 177 can access ABEL reportsthrough computing devices 175. For example, the ABEL reports can beaccessed via a website or through a battery-check application 176executing on the user's computing device 175 (e.g., via an applicationprogramming interface (API)). In certain examples, the user 177 can doso by entering a unique identifier of the power cell, or a vehicleidentification number of the user's vehicle 194. In someimplementations, the user 177 can access ABEL reports of power cellsthrough a web portal, API, or other accessing means to connect with thecomputing system 100.

In further examples, the user interface 125, distributed ledgerinterface 115, and/or power cell interface 105 can correspond to asingle API or multiple APIs that enable communications with thecomputing system 100. Accordingly, the communications systems of theESSs 192, BPDs 193, vehicles 194, manufacturers 196 (e.g., of individualpower cells, power cell packs or strings, the vehicles 194, ESSs 192,BPDs 193), trusted third-party services (not shown), distributed ledger185, and/or the user devices 195 can transmit and/or receive data withthe computing system 100 through an API of the computing system 100.Such data can include notifications and recommendations for enhancingbattery performance, the power cell data, recommendations to repurposeor replace a power cell or power cell package, or ABEL requests andtransmissions of ABEL reports, as described herein.

In examples, a user 177 can comprise any individual or entity that canreceive information that is based on, or otherwise corresponds to, theABEL of any power cell, or to any other type of report that can utilizean output of the system 100. For example, the user 177 can comprise aused vehicle 194 or used battery-powered device 193 buyer interested inthe remaining life of the batteries in an electric or hybrid vehicle 194or a used battery-powered device 193. In further examples, the user 177can represent a current owner of battery-powered device 193 or electricvehicle 194, and can utilize the ABEL report to properly price the usedvehicle 194 or battery-powered device 193 for resale. In still furtherexamples, the user 177 can represent a second life energy storage systementity that assembles second life energy storage systems 192 from usedpower cells (e.g., from vehicles 194 in which the primary end-of-life ofthe vehicles' batteries has been met). In still further examples, theuser 177 can comprise a bank entity, leasing entity, vehicle orbattery-powered device dealership, business entities requiring fleets ofvehicles, rental car and rental truck entities, on-demand electricscooter rental companies, parking structure operators, and the like. Insome variations, the ABEL reports can be generated to provide theseentities with information that facilitates the construction,configuration, or reuse of battery packs or energy storage systems 192comprising individual second (third, or more) life power cells that havethe same or similar ABELs. Such energy storage systems 192 can becomposed of various power cells or batteries of varying chemistries.

In various implementations, the computing system 100 can include abattery optimization engine 130, which can communicate over the powercell interface 105 with vehicle manufacturers, battery-powered devicemanufacturers, and/or power cell manufacturers (collectively“manufacturers 196”) and/or battery management systems, IoT chips, orother communication or processing interfaces of energy storage systems192, battery-powered devices 194, and vehicles 194 (or any other managedbattery or power cell device). The battery optimization engine 130 cananalyze the power cell data and determine an optimal set of settings ofthe batteries that power the various devices 193 or vehicles 194 (e.g.,the optimal settings for battery management systems of the vehicles 194and/or energy storage systems 192) in order to increase or otherwiseimprove ABEL. For example, the battery optimization engine 130 can pushcertain data and/or commands back to, for example, the batterymanagement systems of the power cells for reconfiguration, or to othermanufacturer or third-party systems that can manage such power cells.

In doing so, the battery optimization engine 130 can determine, based onthe power cell data, whether power cells are being used optimally.Certain settings may be changed by the battery optimization engine 130in order to, for example, prolong the life of a battery, reduce theprobability of damage to the battery, and the like. In one example, thebattery optimization engine 130 can receive additional third-partycontextual data, like weather or outdoor/ambient temperatureinformation, traffic information, schedule information of a user 177(e.g., via synchronization with a scheduling application on the user'scomputing device 175), location information from a positioning system ofthe computing device 175 or vehicle 194, and the like. The batteryoptimization engine 130 can reconfigure the battery management systemsof vehicles 194, battery powered devices 193, and/or energy storagesystems 192 to make the power cells run, deploy power, and/or rechargemost optimally or efficiently given the current contextual conditions.For example, if a vehicle 194 operating in hot weather, the batteryoptimization engine 130 can cause the battery management system of thevehicle to be reconfigured for the hot weather (e.g., no fast chargingpermitted).

In certain examples, the battery optimization engine 130 can also act asa warning, notification, and or recommendation system. The ABEL of agiven power cell can be calculated periodically by the EoL machinelearning engine 140. Based, at least in part, on these calculations, thebattery optimization engine 130 can determine optimal battery settingsfor the power cell's battery management system, and can generate andtransmit notifications and/or recommendations to the computing device175 of a user 177 of the vehicle 194, battery-powered device 193, orenergy storage system 192. Such notifications or recommendations canidentify certain actions or practices for users 177 that, if followed,would maximize the ABEL of their power cell (e.g., vehicle batterylife). As an example, the battery optimization engine 130 can transmit anotification to the user 177 (e.g., a push notification from theexecuting battery-check app 176, an email, text message, etc.), whichcan suggest a certain action be performed.

For example, the notification can comprise a suggestion to habituallycharge a vehicle's battery or battery-powered device's battery to 75%,as this can comprise an ideal state of charge for the user's vehicle 194or battery-powered device 193 as determined from the historical ABELscalculated and usage data stored in the data log 147 of the vehicle 194or battery-powered device 193. As another example, the batteryoptimization engine 130 can identify that the user 177 has not used avehicle 194 or battery-powered device 193 for an extended period oftime, and can transmit a recommendation to the computing device 175indicating that minimal suggested usage of the vehicle 194 orbattery-powered device 193 is at least one hour per week to minimizebattery degradation. As another example, based on upcoming weatherconditions indicating hot weather (e.g., as determined from athird-party weather forecasting resource) and the user's scheduleindicating an upcoming vacation, the battery optimization engine 130 cantransmit a notification suggesting that the user 177 store the vehicleindoors at a specified charge (e.g., 80%) during the period of hotweather.

In still further examples, the battery optimization engine 140 canmonitor the power cell data in the power cell data logs 147 (e.g.,stored centrally and/or on the distributed ledger 185) and/or throughdirect communications with the IoT chips or other communicationmechanisms of battery-powered devices 193. For IoT implementations, theIoT chips can include a communication interface (e.g., Wi-Fi, Bluetoothto a central communications hub, etc.), and can be included on nearlyany device requiring or capable of including computational resources.These devices can include virtually any battery powered device 193, suchas sensor devices, construction equipment (e.g., power tools), electricscooters, home appliances, robotic devices (e.g., warehouse robots),office equipment, etc. According to examples described herein, thebattery optimization system 130 can monitor the battery usage,conditions, current rating (e.g., compared to original nameplaterating), etc., in accordance with a set of optimal performance metrics.Such optimal performance metrics can be battery-type specific,battery-chemistry specific, battery-powered device specific, locationspecific (e.g., given average temperatures or weather conditions),time-specific (e.g., given current temperature or weather conditions, orcurrent season), or any combination of the foregoing.

In various implementations, each battery or battery-powered device canbe operable in a set of optimal performance ranges that can correspondto the deployment of power, charging, charging times, nature of usage,storage, and the like. The battery optimization engine 130 can identifywhen a particular battery or battery-powered device 193 operates or isotherwise used outside these set of optimal performance ranges. Inresponse to this determination, the battery optimization system 130 cantransmit a recommendation to the computing device 175 of the user 177(e.g., via the battery-check app 176) indicating a set of suggestionsthat would result in a more optimal use of the batteries of the batterypowered device 193 (e.g., prolong the ABEL and/or increase range, life,or performance of the battery-powered device 193).

It is contemplated that the recommendations triggered by a particularpower cell or BPD 192 operating outside optimal ranges can be applied toboth rechargeable power cells and single use power cells. Furthermore,the techniques described herein with regard to the recommendations foroperational settings and/or adjustments, as well as replacement orrepurposing recommendations can further be applied to direct-powersources, such as solar panels, solar cells, piezoelectric devices, andthe like.

In certain examples, instead of sending recommendations, the batteryoptimization engine 130 can transmit a set of commands directly to theIoT chip or other communication device of the battery-powered device 193to cause a management system of the BPD 193 to adjust one or moresettings corresponding to charging, power deployment, cooling, heating,etc., in order to operate the battery within the set of optimalperformance ranges (e.g., for the purpose of maximizing ABEL).

At times, the battery optimization system 130 may determine that certainbatteries or battery powered-devices 193 are reaching or have reachedtheir approximate end-of-life. For example, after providingrecommendations to users 177 and/or control commands directly to thedevices 193 to maximize ABEL, at some point the batteries of the device193 will reach its actual end-of-life (e.g., which can fluctuate basedon the battery's usage or device on which the batteries areimplemented). At this point or just prior to this point, the batteryoptimization engine 130 can transmit a recommendation to the user device175 of the owner to either repurpose or sell the battery for secondaryuse (e.g., for rechargeable batteries) or recycle and replace thebattery (e.g., for single use batteries).

It is contemplated that the recommendations and/or direct adjustmentcontrols implemented by the battery optimization engine 130 can byparticular useful in certain applications where safety and/orconvenience and efficiency are paramount. One particular application ofthe battery optimization engine 130 is for parking structure operatorswhere each, or nearly each, parking space is monitored by a sensordevice (e.g., a proximity sensor that detects whether a vehicle isparked in its dedicated space). Vehicles or drivers entering suchstructure are provided with information at each entrance or each floorof the structure, which indicates how many available spaces currentlyexist on that floor (e.g., in shopping malls, airports, sporting venues,etc.). Each parking sensor can include a sensor, one or more batteries,and a computer chip (e.g., in IoT chip) that includes a wirelesstransmitter or transceiver. A portion of the data periodicallytransmitted by the parking sensor can comprise charge information forthe one or more batteries of the parking sensor.

The data from each parking sensor can be transmitted to a central hub(e.g., a local database) or a cloud storage resource which the powercell interface 105 can access to compile current power cell data foreach parking sensor. As described herein, each parking sensor can beassociated with a unique identifier and the power cell data compiler 120can store the data with the unique identifier in the power cell datalogs 147 and/or on the distributed ledger 185. The battery optimizationengine 130 can periodically access the data for each parking sensor.Additionally, the EoL ML engine 140 can periodically calculate the ABELfor each battery of each parking sensor.

When the ABEL for any particular parking sensor crosses a certainthreshold (e.g., within 5% of end-of life), the battery optimizationengine 130 can transmit a recommendation to an operator of the parkingstructure (e.g., to a computing device 175 of the operator via thebattery-check app 176) indicating the specific parking sensor. Theoperator may then find the specific parking sensor and change thebattery or replace the sensor with a fully charged sensor. It iscontemplated that this remote monitoring can preempt scenarios in whichparking sensors run out of battery power and inaccurate parkinginformation is provided to patrons of the parking structure, reducingefficiency and potentially impacting business.

In various additional examples, the ABEL reports can facilitatesecondary battery-powered device markets by virtually eliminating fraudand promoting accurate and reliable valuations of battery-powereddevices 193 (e.g., electric scooters) and vehicles 194 (e.g., electricairplanes or cars) for resale on both the owner side and the prospectivebuyer side. Furthermore, by providing end-of-life notifications and/orrecommendations to replace, sell, or recycle batteries, examplesdescribed herein seek to foster circular economy by prolonging effectiveutilization of all raw materials used in the manufacturing of batteriesuntil the chemical and/or physical properties of the batteries no longerallow for further usable capacity.

Methodology

FIG. 2 is a flow chart describing an example method of power celloptimization, according to various examples. In the below description ofFIG. 2 , reference may be made to reference characters representingvarious features shown and described with respect to FIG. 1 .Furthermore, the method described with respect to FIG. 2 may beperformed by an example computing system 100 as shown and described inconnection with FIG. 1 . Referring to FIG. 2 , the computing system 100can access and/or receive power cell data of power cells (200). Incertain implementations, the computing system 100 can receive the powercell data from the battery management systems of the power cells (202).In variations or in addition, the computing system 100 can receive thepower cell data from the power and or vehicle manufacturers 196 (204).Such power cells may be used for any purpose, such as on-board electricor hybrid vehicles 194, energy storage systems 192, etc.

The power cell data can initially be obtained from the power cell and/orvehicle manufacturers 196 (e.g., nameplate data), and an initial datalog 147 for the power cell can be generated by the computing system 100.For example, nameplate data for a new battery can be received from themanufacturer 196 of the battery and/or vehicle. The computing system 100can generate a data log 147 for the new battery by assigning a uniqueidentifier for the new battery, and creating a set of data filesorganized by way of timestamps. Thereafter, new power cell data receivedfor the battery (e.g., from the battery's management system) can beorganized by the computing system 100 in the battery's data log 147.

In various examples, the computing system 100 can compile power celldata in a data log 147 for each power cell (205). The computing system100 can further distribute the power cell data and/or data logs 147 to adistributed ledger 185 (210). In some aspects, each node of thedistributed ledger 185 can include a data compiler 120 and database 145to store power cell data in data logs 147. In such aspects, no centralauthority exists in the power cell optimization system, but rather, thesystem can comprise a distributed architecture for data storage andsecurity. In certain examples, the data log 147 for a given power cellcan comprise metadata based on the accumulated power cell data receivedfrom power cell sources, where the metadata can indicate the optimateoperational ranges for the power cell given the battery chemistry and/orimplementation of the power cell in a particular battery-powered device.In further examples, additional data provided by the battery-powereddevices can also be utilized to determine the ABEL, provide operationalrecommendations, and the like.

The computing system 100 can determine an ABEL for each power cell basedon the historical power cell data for that power cell (215). Thecomputing system 100 can determine the ABEL of a power cell at any giventime. For example, the computing system 100 can update the calculatedABEL of a power cell periodically (e.g., once per week or once permonth), or in response to a user request. As described herein, the ABELcan comprise an approximated battery end of life (e.g., when the batterywill reach 0% capacity or charge) for rechargeable batteries and/orsingle use batteries, and can comprise a determined calendar life, anumber of remaining cycles, a remaining capacity, a remaining state ofhealth, and the like.

In some examples, the computing system 100 can receive data requestsfrom users 177 or the API (220). For example, the users 177 can accessthe computing system 100 and ABEL reports for individual batteriesthrough a lookup interface on a website or application or API. A batteryor battery string or pack may be identified by unique identifier on thebattery nameplate or via a vehicle identification or registration numberof a vehicle 194 or storage system 192. In variations, the uniqueidentifier can comprise a combination of a dedicated batteryidentification number provided by the computing system 100, a password,and the like. In response to a data request, the computing system 100can generate and transmit an ABEL report for the requested power cell tothe requesting user 177 (225). As described herein, the ABEL report canbe generated based on the currently existing historical data for thatpower cell. It is contemplated that over time and with continuouslearning and, the computing system 100 can generate increasingly robustand accurate ABEL reports for individual power cells, no matter thechemistry (e.g., as battery chemistry comprises an input in the ABELdetermination). Thus, based on any new power cell data received, thecomputing system 100 can confirm or adjust machine learning metrics forABEL determinations, making future ABEL calculations for second lifepower cells more robust (230).

In certain implementations, the computing system 100 can furtherdetermine contextual information for the user 177 and/or the user'svehicle 194 (235). As described herein, the contextual information cancomprise weather data, location data indicating the user's currentlocation, temperature data, calendar data indicating the user'sschedule, and the like. In some aspects, these contextual data can bereceived by synchronizing with other applications of the user'scomputing device 175 (e.g., a calendar application, mapping and/ortraffic application, travel application, GPS module, weatherapplication, and the like), or accessing such information fromthird-party resources. Based on the contextual information, thecomputing system 100 can determine a set of optimization settings forthe power cell (240) (e.g., of the user's vehicle 194 or energy storagesystem 192). Such optimization settings can comprise charging settings,discharge settings, maximum and/or minimum charge or capacity settings,power delivery settings, and the like. In certain implementations, thecomputing system 100 can transmit the set of optimization settings tothe battery management system of the power cell for execution, or thebattery-powered device itself (245). Additionally or alternatively, thecomputing system 100 can generate one or more notifications orrecommendations for the user 177, and transmit the notification to theuser 177 (e.g., either to an on-board computer of the vehicle 194 or tothe user's computing device 175), as described herein.

FIG. 3 is a flow chart describing an example method of monitoring powercell data in accordance with a set of optimization metrics and providingoptimization recommendations and/or control commands to increase ormaximize approximate battery end-of-life (ABEL) for batteries orbattery-powered devices, according to example described herein. In thebelow description of FIG. 3 , reference may be made to referencecharacters representing various features shown and described withrespect to FIG. 1 . Furthermore, the method described with respect toFIG. 3 may also be performed by an example computing system 100 as shownand described in connection with FIG. 1 .

Referring to FIG. 3 , the computing system 100 can monitor power celldata for battery powered devices 193 in accordance with a set of optimalbattery performance metrics (300). As provided herein, optimalperformance metrics can be battery-type specific, battery-chemistryspecific, battery-powered device specific, location specific (e.g.,given average temperatures or weather conditions), time-specific (e.g.,given current temperature or weather conditions, or current season), orany combination of the foregoing.

In various implementations, the computing system 100 can monitor thepower cell data dynamically (e.g., in near-real time) (302), or directlyfrom power cell data received from the battery-powered device 193 or acentral hub through which the battery-powered device 193 communicates.As provided herein, the battery-powered devices 193 can include anyproduct or vehicle 194 powered at least partially by one or morebatteries. In variations, the computing system 100 can periodicallyaccess the power cell data from the distributed ledger to determinewhether the various parameters of the battery are operating with the setof optimal battery performance metrics (304). The computing system 100may then determine whether the batteries of the battery-powered device193 is operating within optimal performance ranges corresponding to theset of optimal battery performance metrics (305). If so (307), thecomputing system 100 can continue monitoring the power cell dataaccordingly (300).

However, if not (309), then the computing system 100 can determine a setof optimal battery operation settings or adjustments for thebattery-powered device 193 (310). These optimal battery operationsettings or adjustments can correspond to charging habits (e.g.,charging to no higher than 80% capacity), storage (e.g., indoors versusoutdoors), nature of usage (e.g., high performance usage only attemperatures below 20° Celsius, capacity prior to charging (e.g., nolower than 5%), and the like. The computing system 100 may then transmitthe recommendation(s) to the user device 175 of the user of thebattery-powered device 193 to facilitate optimal usage and charging ofthe battery-power device 193 (e.g., to maximize ABEL) (315).

Additionally or alternatively, the computing system 100 can transmitcontrol commands to the battery-powered device 193 in order to induce oradjust operations to within the optimal set of performance ranges (320).For example, the control commands can cause an IoT chip of abattery-powered device 193 to automatically disconnect from a charger(e.g., disengage a charge switch) when a certain charging capacity isreached (e.g., 93% capacity), and/or automatically connect to a chargerwhen a depleted capacity is reached (e.g., 5% capacity) in order tofacilitate in maximizing ABEL for the batteries of the device 193.

FIG. 4 is a flow chart describing an example method of monitoring powercell data in accordance with a set of end-of-life determination metricsand providing second-life repurposing or battery replacementrecommendations, according to examples described herein. In the belowdescription of FIG. 4 , reference may be made to reference charactersrepresenting various features shown and described with respect to FIG. 1. Furthermore, the method described with respect to FIG. 4 may also beperformed by an example computing system 400 as shown and described inconnection with FIG. 1 . Referring to FIG. 4 , the computing system 100can monitor power cell data for battery-powered devices 193 inaccordance with a set of end-of-life metrics (400). For example, thecomputing system 100 can determine candidate batteries that are nearingtheir calculated ABEL.

In various implementations, the computing system 100 can determinewhether a particular battery or package of batteries are at or near itsend-of-life, end-of-primary life, or ABEL based on the power cell data(e.g., in the distributed ledger or directly from the battery) (405). Ifthe battery has not reached its end-of-life (407), then the process canrepeat to the next iteration of monitoring and end-of-life determination(400). However, if the computing system 100 determines that the batteryhas reached its end-of-life (e.g., the battery is within a certainthreshold of its ABEL) (409), then the computing system 100 can transmita recommendation to the user device 175 of a user 177 of thebattery-powered device 193 indicating the end-of-primary-life of thebattery (410).

In certain examples, the computing system can recommend recycling thebatteries (e.g., for single use batteries) (414). In such examples, thecomputing system 100 can further provide the location or turn-by-turndirections to a battery recycling facility. In variations, the computingsystem 100 can recommend repurposing the battery for secondary life(e.g., for rechargeable batteries) (412). For example, the batteries ofan electric vehicle 194 may reach their end-of-primary life at a certaintime, as determined by the computing system's 100 ABEL calculations andmonitoring. At this point, the charging time versus vehicle range maybecome inconvenient for the user 177. Along these lines, the computingsystem 100 can individually tailor the repurposing recommendation basedon the user's routines (e.g., commuting distance), and transmit therepurposing recommendation at the appropriate time based on the user's177 personal routines, usage habits, or preferences.

When the battery enters its usage as a second life battery, thecomputing system 100 classify it as such in the power cell data logs 147(e.g., on the distributed ledger 185). Thus, any number of second lifebatteries may be available and listed on the distributed ledger 185 withguaranteed ratings and ABELs as determined by the computing system 100.In accordance with examples described herein, the computing system 100can determine, from the distributed ledger 185 and based on the ABELcalculations, a set of qualified second life batteries that arecombinable with the repurposed battery (e.g., having ABELs that arewithin a threshold range of the repurposed battery) (415). It iscontemplated that such batteries can comprise batteries with the same orsimilar ABELs and/or the same or similar battery characteristics (e.g.,same chemistry).

The computing system 100 can provide, through a user interface 125(e.g., using API protocols), information corresponding to second lifebattery packs comprised of second-life batteries based on theirrespective ABELs (420). For example, individuals looking for a batterypack where space and/or weight is not a constraint (e.g., for peak-hourhome use) can be provided with battery packs comprised of multiplebatteries from different primary sources but having similar ABELs. It iscontemplated that the creation of this secondary market for batterypacks through a trusted source having highly accurate and reliable ABELcertifications (e.g., via use of the distributed ledger 185) cansignificantly reduce the cost of battery packs, eliminate uncertaintyregarding used and repurposed batteries, maximize the utilization ofbatteries, and contribute significantly to existing and futureenvironmental policies.

The techniques described throughout the present disclosure can begeneralized for specific power cell types (e.g., lithium ion batteries),combinations of power cell type and operating conditions (e.g., lithiumion batteries used in vehicles or in a particular climate and/orseason), and/or individualized based on user routines and usage habits.Thus, the optimal usage notifications and recommendations, ABEL reports,and repurposing or recycling notifications can be highly tailored basedon any number of factors, including historical usage data (e.g., asindicated in the data logs 147 of the power cells), ambienttemperatures, localized climate (e.g., hot and dry desert climate versuscool and humid climate), current temperature, atmospheric pressure,humidity conditions, manufacturer setting(s) (e.g., a manufacturer'srecommendations for recycling or replacing batteries), user licenseagreement (ULA), warranty agreement, and the like. It is contemplatedthat the ABEL reports provided by the computing system 100 cansupplement or replace such warranties and agreements through the bigdata analytics techniques described herein.

In various examples, the computing system 100 can further providepersonalized recommendations to sell a particular power cell orbattery-powered device. In such examples, the computing system 100 canmonitor a secondary marketplace for power cells or battery-powereddevices and can transmit a notification to an owner or user of a powercell or battery-powered device indicating the current sale or offerprices. For example, when the secondary market for a particularbattery-powered product with a specific ABEL indicates a price premiumthat is a threshold value or percentage (e.g., ten percent) above acurrent valuation based on the ABEL of the battery-powered product, thecomputing system 100 can transmit a notification indicating the premiumto a computing device of the user of the device. The user may thenchoose to sell the device for a profit. As another example, thecomputing system 100 can periodically provide the current market valueof the same or similar battery-powered devices having the same orsimilar ABEL, and provide a comparison to a current valuation of theuser's battery-powered device based on the ABEL of the device.

User Interface Examples

FIGS. 5A and 5B illustrate example user interfaces providing currentbattery state information and notifications (e.g., part of an ABELreport) and a battery-check rating and certificate interface, accordingto examples described herein. FIG. 5A shows a computing device 500 of auser, which can correspond to the user device 175 of the user 177 shownin FIG. 1 . The computing device 500 displays an example user interface550 of a portion of an ABEL report indicating the current battery stateof a particular battery of a battery-powered device 193, and recentnotifications provided through battery monitoring by the computingsystem 100 described in connection with FIG. 1 . In certain aspects, theuser can scroll through additional screens or interface panels to viewthe ABEL of the battery as determined by the computing system 100,and/or various recommendations for maximizing the ABEL of the battery.

FIG. 5B shows a computing device 500 of a user displaying a userinterface screen 555 that provides a battery-check rating andcertificate of the battery of the user's battery-powered device 193,including an ABEL indicator. It is contemplated that the certificateprovides a guarantee as to the reliability of the battery rating, sincethe power cell data for the battery is stored on a distributed ledger185 and the big data and data analytics techniques described hereinprovide for a highly accurate ABEL calculation. Thus, the rating andcertificate shown in the user interface 555 of FIG. 5B can be viewedon-demand by a current owner (e.g., accessed through an API to thecomputing system 100 of FIG. 1 ), prospective buyer, second-life buyer,or original manufacturer of a battery-powered device 193.

Hardware Diagram

FIG. 6 is a block diagram that illustrates a computer system upon whichexamples described herein may be implemented. A computer system 600 canbe implemented on, for example, a server or combination of servers,virtual machine, cloud server(s), cloud environment or platforms. Forexample, the computer system 600 may be implemented as part of a networkservice, such as described in FIGS. 1 through 5B, and/or as a node in adistributed system of nodes (e.g., a distributed ledger 185). In thecontext of FIG. 1 , the computing system 100 may be implemented using acomputer system 600 such as described in connection with FIG. 6 . Thecomputing system 100 may also be implemented using a combination ofmultiple computer systems 600 as described in connection with FIG. 6 .

In one implementation, the computer system 600 includes processingresources 610, a main memory 620, a read-only memory (ROM) 630, astorage device 640, and a communication interface 650. The computersystem 600 includes at least one processor 610 for processinginformation stored in the main memory 620, such as provided by arandom-access memory (RAM) or other dynamic storage device, for storinginformation and instructions which are executable by the processor 610.The main memory 620 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by the processor 610. The computer system 600 may also includethe ROM 630 or other static storage device for storing staticinformation and instructions for the processor 610. A storage device640, such as a magnetic disk or optical disk, is provided for storinginformation and instructions.

The communication interface 650 enables the computer system 600 tocommunicate with one or more networks 680 (e.g., cellular network)through use of the network link (wireless or wired). Using the networklink, the computer system 600 can communicate with one or more computingdevices, one or more servers, one or more databases, and/or one or moreself-driving vehicles. In accordance with examples, the computer system600 receives requests from computing devices of individual users. Theexecutable instructions stored in the memory 630 can include datacompilation instructions 624, which the processing resources 610 canexecute to connect, over the network(s) 680 with battery managementsystems, manufacturers, and other power cell data sources to receivepower cell data 682 from individual power cells and power cell packages.Execution of the data compilation instructions 624 can cause thecomputer system 600 to generate data logs 628 for each power cell andorganize the power cell data 682 therein.

The executable instructions stored in memory 620 can further include EoLmachine learning models 622 executable by the processing resources 610to determine ABELs for power cells based on the compiled power cell datain the data logs 682. Furthermore, the EoL machine learning models 622can be executed by the processors 610 to receive data requests 684 fromusers over the network 680 and, in response, generate and transmit ABELreports 654 to the requesting users over the network 680, as describedherein. The executable instructions can further include batteryoptimization instructions 626, which the processor 610 can execute tomonitor power cell data of battery-powered devices and provide usageoptimization recommendations 656 and/or end-of-life recommendations torepurpose or recycle batteries, as described herein. It is contemplatedthat the instructions and data stored in the memory 620 can be executedby the processor 610 to implement the functions of an example computingsystem 100 of FIG. 1 .

Examples described herein are related to the use of the computer system600 for implementing the techniques described herein. According to oneexample, those techniques are performed by the computer system 600 inresponse to the processor 610 executing one or more sequences of one ormore instructions contained in the main memory 620. Such instructionsmay be read into the main memory 620 from another machine-readablemedium, such as the storage device 640. Execution of the sequences ofinstructions contained in the main memory 620 causes the processor 610to perform the process steps described herein. In alternativeimplementations, hard-wired circuitry may be used in place of or incombination with software instructions to implement examples describedherein. Thus, the examples described are not limited to any specificcombination of hardware circuitry and software.

It is contemplated for examples described herein to extend to individualelements and concepts described herein, independently of other concepts,ideas or systems, as well as for examples to include combinations ofelements recited anywhere in this application. Although examples aredescribed in detail herein with reference to the accompanying drawings,it is to be understood that the concepts are not limited to thoseprecise examples. As such, many modifications and variations will beapparent to practitioners skilled in this art. Accordingly, it isintended that the scope of the concepts be defined by the followingclaims and their equivalents. Furthermore, it is contemplated that aparticular feature described either individually or as part of anexample can be combined with other individually described features, orparts of other examples, even if the other features and examples make nomentioned of the particular feature. Thus, the absence of describingcombinations should not preclude claiming rights to such combinations.

What is claimed is:
 1. A computing system comprising: a networkcommunication interface communicating, over one or more networks, withpower cell sources; one or more processors; and one or more memoryresources storing instructions that, when executed by the one or moreprocessors, cause the computing system to: receive, over the one or morenetworks, power cell data from the power cell sources; based, at leastin part, on the power cell data specific to a given power cell of thepower cell sources, determine an approximate battery end of life (ABEL)for the given power cell; distribute the power cell data to adistributed ledger for storage with a unique identifier for the givenpower cell; store the ABEL of the given power cell on the distributedledger with the unique identifier; and generate an ABEL report for thegiven power cell based on the determined ABEL for the given power cell;wherein the ABEL report comprises a trusted certificate of accuracybased on the power cell data and the ABEL being stored on thedistributed ledger.
 2. The computing system of claim 1, wherein thepower cell data is received from battery management systems.
 3. Thecomputing system of claim 2, wherein the battery management systems areincluded on at least one of electric vehicles, hybrid vehicles, orenergy storage systems.
 4. The computing system of claim 1, wherein thepower cell data is received from power cell manufacturers.
 5. Thecomputing system of claim 1, wherein the executed instructions furthercause the computing system to: determine contextual information for atleast one of the given power cell or a user of the given power cell, thecontextual information indicating at least one of a current location ofthe user, weather information, a schedule of the user, or trafficconditions.
 6. The computing system of claim 5, wherein the executedinstructions further cause the computing system to: based on thecontextual information and at least one of the received power cell dataor the determined ABEL, determine a set of optimization configurationsfor the given power cell.
 7. The computing system of claim 6, whereinthe executed instructions further cause the computing system to:transmit the set of optimization configurations to a battery managementsystem of the given power cell for execution.
 8. The computing system ofclaim 6, wherein the executed instructions further cause the computingsystem to: transmit a notification to a computing device of the user,the notification indicating one or more warnings or recommendations toeither maximize the ABEL for the given power cell or prevent damage tothe given power cell.
 9. The computing system of claim 1, wherein theexecuted instructions cause the computing system to (i) receive, overthe one or more networks, updated power cell data from the power cellsources periodically, and (ii) distribute the updated power cell data tothe distributed ledger with the unique identifier for the given powercell.
 10. The computing system of claim 9, wherein the executedinstructions further cause the computing system to: determine, based onthe updated power cell data for the given power cell, that the givenpower cell is operating outside a set of optimal performance ranges. 11.The computing system of claim 10, wherein the executed instructionsfurther cause the computing system to: transmit, over the one or morenetworks, a set of recommendations to a computing device of a user of abattery-powered device being powered by the given power cell, the set ofrecommendations being provided to maximize the ABEL of the given powercell.
 12. The computing system of claim 10, wherein the executedinstructions further cause the computing system to: transmit, over theone or more networks, a set of control commands to a battery-powereddevice being powered by the given power cell, the set of controlcommands causing the battery-powered device to adjust one or moreoperational settings in order to maximize the ABEL of the given powercell.
 13. The computing system of claim 12, wherein the executedinstructions cause the computing system to transmit the set of controlcommands to an Internet of Things (IoT) chip of the battery-powereddevice.
 14. The computing system of claim 10, wherein the executedinstructions further cause the computing system to: determine, based onthe updated power cell data for the given power cell, that the givenpower cell is within a certain threshold of its ABEL; and transmit, overthe one or more networks, a notification to a computing device of a userof a battery-powered device being powered by the given power cell, thenotification indicating that the given power cell is within the certainthreshold of its ABEL.
 15. The computing system of claim 14, wherein thenotification comprises a recommendation to repurpose the given powercell as a second-life power cell or recycle the given power cell. 16.The computing system of claim 15, wherein the executed instructionsfurther cause the computing system to: determine, from the distributedledger, one or more additional second life power cells that each have anABEL that is within a threshold range of the given power cell; andprovide, via a user interface for second-life power cell users, a set ofsecond-life power cell packages each comprised of second life powercells that each have an ABEL that is within a threshold range of eachother.
 17. The computing system of claim 1, wherein the power cell datareceived from each of the power cell sources comprise at least one of(i) a type of battery-powered device that comprises the power cellsource, or (ii) a battery chemistry of a power cell that powers thepower cell source.
 18. The computing system of claim 1, wherein theexecuted instructions further cause the computing system to: receiveadditional data provided by the power cell sources, the additional datacomprising at least one of telemetry data, diagnostic data, or sensordata from the power cell sources; wherein the executed instructionscause the computing system to further determine the ABEL of the givenpower cell based on the additional data received from the power cellsources.
 19. A non-transitory computer-readable medium storinginstructions that, when executed by one or more processors, cause theone or more processors to: receive, over one or more networks, powercell data from one or more power cell sources; based, at least in part,on the power cell data specific to a given power cell of the one or morepower cell sources, determine an approximate battery end of life (ABEL)for the given power cell; determine contextual information for at leastone of the given power cell or a user of the given power cell, thecontextual information indicating at least one of a current location ofthe user, weather information, a schedule of the user, or trafficconditions; and based on the contextual information and at least one ofthe received power cell data or the determined ABEL, determine a set ofoptimization configurations for the given power cell.
 20. Acomputer-implemented method of maximizing approximate battery end oflife (ABEL) of power cells, the method being performed by one or moreprocessors and comprising: receiving, over one or more networks, powercell data from one or more power cell sources; based, at least in part,on the power cell data specific to a given power cell of the one or morepower cell sources, determining an approximate battery end of life(ABEL) for the given power cell; distributing the power cell data to adistributed ledger for storage with a unique identifier for the givenpower cell; and storing the ABEL of the given power cell on thedistributed ledger with the unique identifier; receiving, over the oneor more networks, updated power cell data from the power cell sourcesperiodically; distributing the updated power cell data to thedistributed ledger with the unique identifier for the given power cell;determining, based on the updated power cell data for the given powercell, that the given power cell is operating outside a set of optimalperformance ranges; and transmitting, over the one or more networks, aset of recommendations to a computing device of a user of abattery-powered device being powered by the given power cell, the set ofrecommendations being provided to maximize the ABEL of the given powercell.